France's union of private radiologists, Fédération nationale des médecins radiologues (FNMR), has voiced concern at the most recent draft law on the financing of social security for 2023.
According to the FNMR, article 26 in the draft text gives the director general of France's National Medical Insurance Fund Union (Union Nationale des Caisses d'Assurance Maladie, UNCAM) sole power to impose cost studies borne by CT, MRI, and PET operators.
In the statement released on 29 September, the FNMR pointed out that in 2007, the health insurance representative unions agreed to study all medical imaging costs, all techniques included, for the best care of patients, in amendment 23 to the 2005 medical agreement. This action was then not implemented.
The FNMR demands the removal of article 26 so that cost studies can be jointly piloted by the National Medical Insurance Fund (Caisse Nationale d'Assurance Maladie, CNAM) and the FNMR. In addition, the FNMR repeated its call for the repeal of Article 99 of the 2017 social security financing law, which gives even more power to the CNAM's director general to unilaterally modify tariffs for MRI and PET according to the results of the audits.
The FNMR noted that on the eve of "Pink October," the month dedicated to breast cancer screening awareness, the government was sending "a very bad signal," which discouraged associative and entrepreneurial initiatives, as well as collective awareness: Cancers and other pathologies that are not screened due to lack of resources will result in medical expenses and a budgetary burden that penalizes patient care, the union stated. It further called on the CNAM to choose between authoritarianism and partnership.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
